Overview

Brought to you by YData

Dataset statistics

Number of variables23
Number of observations21210
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.6 MiB
Average record size in memory180.0 B

Variable types

Numeric18
DateTime1
Categorical4

Alerts

price is highly overall correlated with grade and 3 other fieldsHigh correlation
bedrooms is highly overall correlated with bathrooms and 2 other fieldsHigh correlation
bathrooms is highly overall correlated with age and 7 other fieldsHigh correlation
sqft_living is highly overall correlated with bathrooms and 5 other fieldsHigh correlation
sqft_lot is highly overall correlated with sqft_lot15High correlation
floors is highly overall correlated with age and 3 other fieldsHigh correlation
grade is highly overall correlated with age and 6 other fieldsHigh correlation
sqft_above is highly overall correlated with bathrooms and 6 other fieldsHigh correlation
yr_built is highly overall correlated with age and 3 other fieldsHigh correlation
zipcode is highly overall correlated with longHigh correlation
long is highly overall correlated with zipcodeHigh correlation
sqft_living15 is highly overall correlated with bathrooms and 4 other fieldsHigh correlation
sqft_lot15 is highly overall correlated with sqft_lotHigh correlation
age is highly overall correlated with bathrooms and 3 other fieldsHigh correlation
condition is highly overall correlated with yr_built and 1 other fieldsHigh correlation
sqft_basement is highly overall correlated with sqft_livingHigh correlation
lat is highly overall correlated with price and 1 other fieldsHigh correlation
waterfront is highly imbalanced (96.6%) Imbalance
view is highly imbalanced (74.0%) Imbalance
id has unique values Unique
sqft_basement has 12955 (61.1%) zeros Zeros
yr_renovated has 20369 (96.0%) zeros Zeros
age has 530 (2.5%) zeros Zeros

Reproduction

Analysis started2024-11-29 08:29:16.949004
Analysis finished2024-11-29 08:29:33.626008
Duration16.68 seconds
Software versionydata-profiling vv4.12.0
Download configurationconfig.json

Variables

id
Real number (ℝ)

Unique 

Distinct21210
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.5876091 × 109
Minimum1000102
Maximum9.9000002 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size165.8 KiB
2024-11-29T16:29:33.669969image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum1000102
5-th percentile5.1000251 × 108
Q12.1250591 × 109
median3.9050004 × 109
Q37.3124001 × 109
95-th percentile9.2973011 × 109
Maximum9.9000002 × 109
Range9.8990001 × 109
Interquartile range (IQR)5.187341 × 109

Descriptive statistics

Standard deviation2.8763082 × 109
Coefficient of variation (CV)0.62697326
Kurtosis-1.262218
Mean4.5876091 × 109
Median Absolute Deviation (MAD)2.4066965 × 109
Skewness0.23959282
Sum9.7303189 × 1013
Variance8.273149 × 1018
MonotonicityNot monotonic
2024-11-29T16:29:33.727167image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7129300520 1
 
< 0.1%
2064800610 1
 
< 0.1%
3213200180 1
 
< 0.1%
1545807920 1
 
< 0.1%
7524950730 1
 
< 0.1%
2592220040 1
 
< 0.1%
6852700477 1
 
< 0.1%
3630160610 1
 
< 0.1%
1504800097 1
 
< 0.1%
8687800065 1
 
< 0.1%
Other values (21200) 21200
> 99.9%
ValueCountFrequency (%)
1000102 1
< 0.1%
1200019 1
< 0.1%
1200021 1
< 0.1%
2800031 1
< 0.1%
3600057 1
< 0.1%
3600072 1
< 0.1%
3800008 1
< 0.1%
5200087 1
< 0.1%
6200017 1
< 0.1%
7200080 1
< 0.1%
ValueCountFrequency (%)
9900000190 1
< 0.1%
9895000040 1
< 0.1%
9842300540 1
< 0.1%
9842300485 1
< 0.1%
9842300095 1
< 0.1%
9842300036 1
< 0.1%
9839301165 1
< 0.1%
9839301060 1
< 0.1%
9839301055 1
< 0.1%
9839300875 1
< 0.1%

date
Date

Distinct372
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Memory size165.8 KiB
Minimum2014-05-02 00:00:00
Maximum2015-05-27 00:00:00
2024-11-29T16:29:33.780735image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-29T16:29:33.866650image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

price
Real number (ℝ)

High correlation 

Distinct3919
Distinct (%)18.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean526238.97
Minimum75000
Maximum4489000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size165.8 KiB
2024-11-29T16:29:33.923793image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum75000
5-th percentile212633.55
Q1322000
median450000
Q3637500
95-th percentile1085775
Maximum4489000
Range4414000
Interquartile range (IQR)315500

Descriptive statistics

Standard deviation313627.9
Coefficient of variation (CV)0.59598
Kurtosis12.334217
Mean526238.97
Median Absolute Deviation (MAD)150000
Skewness2.6013355
Sum1.1161529 × 1010
Variance9.8362462 × 1010
MonotonicityNot monotonic
2024-11-29T16:29:33.976234image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
450000 171
 
0.8%
350000 167
 
0.8%
550000 157
 
0.7%
500000 151
 
0.7%
425000 148
 
0.7%
325000 146
 
0.7%
400000 143
 
0.7%
375000 137
 
0.6%
300000 131
 
0.6%
525000 128
 
0.6%
Other values (3909) 19731
93.0%
ValueCountFrequency (%)
75000 1
< 0.1%
78000 1
< 0.1%
80000 1
< 0.1%
81000 1
< 0.1%
82500 1
< 0.1%
83000 1
< 0.1%
84000 1
< 0.1%
85000 1
< 0.1%
89000 1
< 0.1%
89950 1
< 0.1%
ValueCountFrequency (%)
4489000 1
< 0.1%
4000000 1
< 0.1%
3710000 1
< 0.1%
3650000 1
< 0.1%
3635000 1
< 0.1%
3418800 1
< 0.1%
3400000 1
< 0.1%
3204000 1
< 0.1%
3200000 1
< 0.1%
3100000 1
< 0.1%

bedrooms
Real number (ℝ)

High correlation 

Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.3589816
Minimum0
Maximum8
Zeros13
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size165.8 KiB
2024-11-29T16:29:34.021443image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q13
median3
Q34
95-th percentile5
Maximum8
Range8
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.88923322
Coefficient of variation (CV)0.26473298
Kurtosis0.95191765
Mean3.3589816
Median Absolute Deviation (MAD)1
Skewness0.37149875
Sum71244
Variance0.79073573
MonotonicityNot monotonic
2024-11-29T16:29:34.062674image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
3 9691
45.7%
4 6778
32.0%
2 2722
 
12.8%
5 1528
 
7.2%
6 243
 
1.1%
1 190
 
0.9%
7 33
 
0.2%
0 13
 
0.1%
8 12
 
0.1%
ValueCountFrequency (%)
0 13
 
0.1%
1 190
 
0.9%
2 2722
 
12.8%
3 9691
45.7%
4 6778
32.0%
5 1528
 
7.2%
6 243
 
1.1%
7 33
 
0.2%
8 12
 
0.1%
ValueCountFrequency (%)
8 12
 
0.1%
7 33
 
0.2%
6 243
 
1.1%
5 1528
 
7.2%
4 6778
32.0%
3 9691
45.7%
2 2722
 
12.8%
1 190
 
0.9%
0 13
 
0.1%

bathrooms
Real number (ℝ)

High correlation 

Distinct25
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.1007426
Minimum0
Maximum6.5
Zeros10
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size165.8 KiB
2024-11-29T16:29:34.106344image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11.5
median2.25
Q32.5
95-th percentile3.5
Maximum6.5
Range6.5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.74420137
Coefficient of variation (CV)0.35425634
Kurtosis0.26798102
Mean2.1007426
Median Absolute Deviation (MAD)0.5
Skewness0.29959646
Sum44556.75
Variance0.55383567
MonotonicityNot monotonic
2024-11-29T16:29:34.150778image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
2.5 5332
25.1%
1 3789
17.9%
1.75 3011
14.2%
2.25 2027
 
9.6%
2 1910
 
9.0%
1.5 1429
 
6.7%
2.75 1169
 
5.5%
3 736
 
3.5%
3.5 708
 
3.3%
3.25 564
 
2.7%
Other values (15) 535
 
2.5%
ValueCountFrequency (%)
0 10
 
< 0.1%
0.5 4
 
< 0.1%
0.75 67
 
0.3%
1 3789
17.9%
1.25 9
 
< 0.1%
1.5 1429
 
6.7%
1.75 3011
14.2%
2 1910
 
9.0%
2.25 2027
 
9.6%
2.5 5332
25.1%
ValueCountFrequency (%)
6.5 1
 
< 0.1%
6 2
 
< 0.1%
5.75 1
 
< 0.1%
5.5 3
 
< 0.1%
5.25 9
 
< 0.1%
5 15
 
0.1%
4.75 13
 
0.1%
4.5 78
0.4%
4.25 62
0.3%
4 116
0.5%

sqft_living
Real number (ℝ)

High correlation 

Distinct967
Distinct (%)4.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2053.7525
Minimum290
Maximum7320
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size165.8 KiB
2024-11-29T16:29:34.200112image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum290
5-th percentile940
Q11420
median1900
Q32530
95-th percentile3673.1
Maximum7320
Range7030
Interquartile range (IQR)1110

Descriptive statistics

Standard deviation857.03547
Coefficient of variation (CV)0.41730222
Kurtosis1.3481802
Mean2053.7525
Median Absolute Deviation (MAD)540
Skewness0.99409077
Sum43560090
Variance734509.8
MonotonicityNot monotonic
2024-11-29T16:29:34.254007image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1300 135
 
0.6%
1440 133
 
0.6%
1400 132
 
0.6%
1800 128
 
0.6%
1660 128
 
0.6%
1820 127
 
0.6%
1560 124
 
0.6%
1010 124
 
0.6%
1540 122
 
0.6%
1480 122
 
0.6%
Other values (957) 19935
94.0%
ValueCountFrequency (%)
290 1
< 0.1%
370 1
< 0.1%
380 1
< 0.1%
384 1
< 0.1%
390 2
< 0.1%
410 1
< 0.1%
420 2
< 0.1%
430 1
< 0.1%
440 1
< 0.1%
460 1
< 0.1%
ValueCountFrequency (%)
7320 1
< 0.1%
7080 1
< 0.1%
6810 1
< 0.1%
6640 1
< 0.1%
6563 1
< 0.1%
6500 1
< 0.1%
6430 1
< 0.1%
6380 1
< 0.1%
6370 1
< 0.1%
6330 1
< 0.1%

sqft_lot
Real number (ℝ)

High correlation 

Distinct9638
Distinct (%)45.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14422.315
Minimum520
Maximum1164794
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size165.8 KiB
2024-11-29T16:29:34.308976image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum520
5-th percentile1779
Q15020.25
median7570.5
Q310522.5
95-th percentile41807.85
Maximum1164794
Range1164274
Interquartile range (IQR)5502.25

Descriptive statistics

Standard deviation36749.945
Coefficient of variation (CV)2.5481308
Kurtosis217.84818
Mean14422.315
Median Absolute Deviation (MAD)2590.5
Skewness11.582157
Sum3.058973 × 108
Variance1.3505584 × 109
MonotonicityNot monotonic
2024-11-29T16:29:35.397763image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5000 354
 
1.7%
6000 282
 
1.3%
4000 248
 
1.2%
7200 216
 
1.0%
4800 118
 
0.6%
7500 117
 
0.6%
4500 112
 
0.5%
8400 109
 
0.5%
9600 108
 
0.5%
3600 102
 
0.5%
Other values (9628) 19444
91.7%
ValueCountFrequency (%)
520 1
< 0.1%
572 1
< 0.1%
600 1
< 0.1%
609 1
< 0.1%
635 1
< 0.1%
638 1
< 0.1%
649 2
< 0.1%
651 1
< 0.1%
675 1
< 0.1%
676 1
< 0.1%
ValueCountFrequency (%)
1164794 1
< 0.1%
1074218 1
< 0.1%
1024068 1
< 0.1%
982998 1
< 0.1%
982278 1
< 0.1%
843309 1
< 0.1%
715690 1
< 0.1%
641203 1
< 0.1%
623779 1
< 0.1%
577605 1
< 0.1%

floors
Real number (ℝ)

High correlation 

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.4926214
Minimum1
Maximum3.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size165.8 KiB
2024-11-29T16:29:35.440773image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1.5
Q32
95-th percentile2
Maximum3.5
Range2.5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.53915024
Coefficient of variation (CV)0.36121031
Kurtosis-0.48425389
Mean1.4926214
Median Absolute Deviation (MAD)0.5
Skewness0.61803565
Sum31658.5
Variance0.29068298
MonotonicityNot monotonic
2024-11-29T16:29:35.479473image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
1 10507
49.5%
2 8083
38.1%
1.5 1871
 
8.8%
3 599
 
2.8%
2.5 143
 
0.7%
3.5 7
 
< 0.1%
ValueCountFrequency (%)
1 10507
49.5%
1.5 1871
 
8.8%
2 8083
38.1%
2.5 143
 
0.7%
3 599
 
2.8%
3.5 7
 
< 0.1%
ValueCountFrequency (%)
3.5 7
 
< 0.1%
3 599
 
2.8%
2.5 143
 
0.7%
2 8083
38.1%
1.5 1871
 
8.8%
1 10507
49.5%

waterfront
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size165.8 KiB
0
21134 
1
 
76

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters21210
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 21134
99.6%
1 76
 
0.4%

Length

2024-11-29T16:29:35.520690image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-29T16:29:35.559345image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
0 21134
99.6%
1 76
 
0.4%

Most occurring characters

ValueCountFrequency (%)
0 21134
99.6%
1 76
 
0.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 21210
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 21134
99.6%
1 76
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 21210
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 21134
99.6%
1 76
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 21210
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 21134
99.6%
1 76
 
0.4%

view
Categorical

Imbalance 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size165.8 KiB
0
19273 
2
 
934
3
 
469
1
 
327
4
 
207

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters21210
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 19273
90.9%
2 934
 
4.4%
3 469
 
2.2%
1 327
 
1.5%
4 207
 
1.0%

Length

2024-11-29T16:29:35.596657image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-29T16:29:35.636675image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
0 19273
90.9%
2 934
 
4.4%
3 469
 
2.2%
1 327
 
1.5%
4 207
 
1.0%

Most occurring characters

ValueCountFrequency (%)
0 19273
90.9%
2 934
 
4.4%
3 469
 
2.2%
1 327
 
1.5%
4 207
 
1.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 21210
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 19273
90.9%
2 934
 
4.4%
3 469
 
2.2%
1 327
 
1.5%
4 207
 
1.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 21210
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 19273
90.9%
2 934
 
4.4%
3 469
 
2.2%
1 327
 
1.5%
4 207
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 21210
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 19273
90.9%
2 934
 
4.4%
3 469
 
2.2%
1 327
 
1.5%
4 207
 
1.0%

condition
Categorical

High correlation 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size165.8 KiB
3
13763 
4
5596 
5
1661 
2
 
162
1
 
28

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters21210
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row3
3rd row3
4th row5
5th row3

Common Values

ValueCountFrequency (%)
3 13763
64.9%
4 5596
26.4%
5 1661
 
7.8%
2 162
 
0.8%
1 28
 
0.1%

Length

2024-11-29T16:29:35.678860image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-29T16:29:35.719054image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
3 13763
64.9%
4 5596
26.4%
5 1661
 
7.8%
2 162
 
0.8%
1 28
 
0.1%

Most occurring characters

ValueCountFrequency (%)
3 13763
64.9%
4 5596
26.4%
5 1661
 
7.8%
2 162
 
0.8%
1 28
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 21210
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3 13763
64.9%
4 5596
26.4%
5 1661
 
7.8%
2 162
 
0.8%
1 28
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 21210
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3 13763
64.9%
4 5596
26.4%
5 1661
 
7.8%
2 162
 
0.8%
1 28
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 21210
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3 13763
64.9%
4 5596
26.4%
5 1661
 
7.8%
2 162
 
0.8%
1 28
 
0.1%

grade
Real number (ℝ)

High correlation 

Distinct12
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.6365394
Minimum1
Maximum13
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size165.8 KiB
2024-11-29T16:29:35.758044image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile6
Q17
median7
Q38
95-th percentile10
Maximum13
Range12
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.1394594
Coefficient of variation (CV)0.14921149
Kurtosis0.99351896
Mean7.6365394
Median Absolute Deviation (MAD)1
Skewness0.68448489
Sum161971
Variance1.2983678
MonotonicityNot monotonic
2024-11-29T16:29:35.797516image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
7 8880
41.9%
8 6022
28.4%
9 2584
 
12.2%
6 1986
 
9.4%
10 1086
 
5.1%
11 331
 
1.6%
5 232
 
1.1%
12 52
 
0.2%
4 29
 
0.1%
13 4
 
< 0.1%
Other values (2) 4
 
< 0.1%
ValueCountFrequency (%)
1 1
 
< 0.1%
3 3
 
< 0.1%
4 29
 
0.1%
5 232
 
1.1%
6 1986
 
9.4%
7 8880
41.9%
8 6022
28.4%
9 2584
 
12.2%
10 1086
 
5.1%
11 331
 
1.6%
ValueCountFrequency (%)
13 4
 
< 0.1%
12 52
 
0.2%
11 331
 
1.6%
10 1086
 
5.1%
9 2584
 
12.2%
8 6022
28.4%
7 8880
41.9%
6 1986
 
9.4%
5 232
 
1.1%
4 29
 
0.1%

sqft_above
Real number (ℝ)

High correlation 

Distinct900
Distinct (%)4.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1770.6566
Minimum290
Maximum7320
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size165.8 KiB
2024-11-29T16:29:35.843333image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum290
5-th percentile850
Q11190
median1560
Q32190
95-th percentile3320
Maximum7320
Range7030
Interquartile range (IQR)1000

Descriptive statistics

Standard deviation791.7542
Coefficient of variation (CV)0.44715287
Kurtosis1.6482237
Mean1770.6566
Median Absolute Deviation (MAD)450
Skewness1.2112886
Sum37555627
Variance626874.71
MonotonicityNot monotonic
2024-11-29T16:29:35.895382image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1300 209
 
1.0%
1010 204
 
1.0%
1200 203
 
1.0%
1220 186
 
0.9%
1140 183
 
0.9%
1400 179
 
0.8%
1180 176
 
0.8%
1060 176
 
0.8%
1340 174
 
0.8%
1250 173
 
0.8%
Other values (890) 19347
91.2%
ValueCountFrequency (%)
290 1
< 0.1%
370 1
< 0.1%
380 1
< 0.1%
384 1
< 0.1%
390 2
< 0.1%
410 1
< 0.1%
420 2
< 0.1%
430 1
< 0.1%
440 1
< 0.1%
460 1
< 0.1%
ValueCountFrequency (%)
7320 1
< 0.1%
6640 1
< 0.1%
6430 1
< 0.1%
6380 1
< 0.1%
6370 1
< 0.1%
6110 2
< 0.1%
6070 1
< 0.1%
5990 1
< 0.1%
5844 1
< 0.1%
5760 1
< 0.1%

sqft_basement
Real number (ℝ)

High correlation  Zeros 

Distinct278
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean283.09585
Minimum0
Maximum2850
Zeros12955
Zeros (%)61.1%
Negative0
Negative (%)0.0%
Memory size165.8 KiB
2024-11-29T16:29:35.946088image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q3550
95-th percentile1160
Maximum2850
Range2850
Interquartile range (IQR)550

Descriptive statistics

Standard deviation426.5906
Coefficient of variation (CV)1.5068769
Kurtosis1.3078512
Mean283.09585
Median Absolute Deviation (MAD)0
Skewness1.4206391
Sum6004463
Variance181979.54
MonotonicityNot monotonic
2024-11-29T16:29:35.996384image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 12955
61.1%
600 217
 
1.0%
700 214
 
1.0%
500 210
 
1.0%
800 205
 
1.0%
400 184
 
0.9%
900 143
 
0.7%
1000 142
 
0.7%
300 140
 
0.7%
200 106
 
0.5%
Other values (268) 6694
31.6%
ValueCountFrequency (%)
0 12955
61.1%
10 1
 
< 0.1%
20 1
 
< 0.1%
40 4
 
< 0.1%
50 11
 
0.1%
60 10
 
< 0.1%
65 1
 
< 0.1%
70 7
 
< 0.1%
80 20
 
0.1%
90 21
 
0.1%
ValueCountFrequency (%)
2850 1
< 0.1%
2720 1
< 0.1%
2610 1
< 0.1%
2570 1
< 0.1%
2350 1
< 0.1%
2330 1
< 0.1%
2250 1
< 0.1%
2220 2
< 0.1%
2200 1
< 0.1%
2196 1
< 0.1%

yr_built
Real number (ℝ)

High correlation 

Distinct116
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1971.1416
Minimum1900
Maximum2015
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size165.8 KiB
2024-11-29T16:29:36.047223image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum1900
5-th percentile1915
Q11952
median1975
Q31997
95-th percentile2011
Maximum2015
Range115
Interquartile range (IQR)45

Descriptive statistics

Standard deviation29.317482
Coefficient of variation (CV)0.014873351
Kurtosis-0.64602845
Mean1971.1416
Median Absolute Deviation (MAD)23
Skewness-0.47422058
Sum41807913
Variance859.51473
MonotonicityNot monotonic
2024-11-29T16:29:36.102099image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2014 557
 
2.6%
2006 450
 
2.1%
2005 445
 
2.1%
2004 424
 
2.0%
2003 419
 
2.0%
1977 414
 
2.0%
2007 409
 
1.9%
1978 381
 
1.8%
1968 378
 
1.8%
2008 362
 
1.7%
Other values (106) 16971
80.0%
ValueCountFrequency (%)
1900 86
0.4%
1901 29
 
0.1%
1902 27
 
0.1%
1903 43
0.2%
1904 43
0.2%
1905 73
0.3%
1906 89
0.4%
1907 63
0.3%
1908 85
0.4%
1909 91
0.4%
ValueCountFrequency (%)
2015 38
 
0.2%
2014 557
2.6%
2013 198
 
0.9%
2012 169
 
0.8%
2011 130
 
0.6%
2010 141
 
0.7%
2009 225
1.1%
2008 362
1.7%
2007 409
1.9%
2006 450
2.1%

yr_renovated
Real number (ℝ)

Zeros 

Distinct70
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean79.146865
Minimum0
Maximum2015
Zeros20369
Zeros (%)96.0%
Negative0
Negative (%)0.0%
Memory size165.8 KiB
2024-11-29T16:29:36.155605image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum2015
Range2015
Interquartile range (IQR)0

Descriptive statistics

Standard deviation389.53362
Coefficient of variation (CV)4.9216557
Kurtosis20.272701
Mean79.146865
Median Absolute Deviation (MAD)0
Skewness4.7190157
Sum1678705
Variance151736.44
MonotonicityNot monotonic
2024-11-29T16:29:36.210507image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 20369
96.0%
2014 91
 
0.4%
2013 36
 
0.2%
2005 35
 
0.2%
2007 35
 
0.2%
2000 33
 
0.2%
2003 31
 
0.1%
2004 26
 
0.1%
2006 21
 
0.1%
1989 20
 
0.1%
Other values (60) 513
 
2.4%
ValueCountFrequency (%)
0 20369
96.0%
1934 1
 
< 0.1%
1940 2
 
< 0.1%
1944 1
 
< 0.1%
1945 3
 
< 0.1%
1946 2
 
< 0.1%
1948 1
 
< 0.1%
1950 2
 
< 0.1%
1951 1
 
< 0.1%
1953 3
 
< 0.1%
ValueCountFrequency (%)
2015 14
 
0.1%
2014 91
0.4%
2013 36
 
0.2%
2012 11
 
0.1%
2011 13
 
0.1%
2010 17
 
0.1%
2009 20
 
0.1%
2008 16
 
0.1%
2007 35
 
0.2%
2006 21
 
0.1%

zipcode
Real number (ℝ)

High correlation 

Distinct70
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean98077.883
Minimum98001
Maximum98199
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size165.8 KiB
2024-11-29T16:29:36.264735image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum98001
5-th percentile98004
Q198033
median98065
Q398117
95-th percentile98177
Maximum98199
Range198
Interquartile range (IQR)84

Descriptive statistics

Standard deviation53.360672
Coefficient of variation (CV)0.00054406427
Kurtosis-0.84862513
Mean98077.883
Median Absolute Deviation (MAD)42
Skewness0.40528652
Sum2.0802319 × 109
Variance2847.3613
MonotonicityNot monotonic
2024-11-29T16:29:36.322255image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
98103 598
 
2.8%
98038 585
 
2.8%
98115 575
 
2.7%
98052 570
 
2.7%
98117 548
 
2.6%
98042 546
 
2.6%
98034 539
 
2.5%
98118 497
 
2.3%
98023 489
 
2.3%
98133 485
 
2.3%
Other values (60) 15778
74.4%
ValueCountFrequency (%)
98001 359
1.7%
98002 197
0.9%
98003 276
1.3%
98004 301
1.4%
98005 168
 
0.8%
98006 471
2.2%
98007 139
 
0.7%
98008 276
1.3%
98010 98
 
0.5%
98011 194
0.9%
ValueCountFrequency (%)
98199 309
1.5%
98198 269
1.3%
98188 135
 
0.6%
98178 257
1.2%
98177 239
1.1%
98168 264
1.2%
98166 242
1.1%
98155 438
2.1%
98148 56
 
0.3%
98146 279
1.3%

lat
Real number (ℝ)

High correlation 

Distinct5023
Distinct (%)23.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean47.560012
Minimum47.1559
Maximum47.7776
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size165.8 KiB
2024-11-29T16:29:36.379093image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum47.1559
5-th percentile47.3103
Q147.470325
median47.5719
Q347.6782
95-th percentile47.7496
Maximum47.7776
Range0.6217
Interquartile range (IQR)0.207875

Descriptive statistics

Standard deviation0.13875891
Coefficient of variation (CV)0.0029175541
Kurtosis-0.68311691
Mean47.560012
Median Absolute Deviation (MAD)0.105
Skewness-0.48474394
Sum1008747.9
Variance0.019254034
MonotonicityNot monotonic
2024-11-29T16:29:36.432109image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
47.5322 17
 
0.1%
47.5491 17
 
0.1%
47.6624 17
 
0.1%
47.6846 17
 
0.1%
47.6886 16
 
0.1%
47.6955 16
 
0.1%
47.6711 16
 
0.1%
47.686 15
 
0.1%
47.5402 15
 
0.1%
47.6647 15
 
0.1%
Other values (5013) 21049
99.2%
ValueCountFrequency (%)
47.1559 1
< 0.1%
47.1593 1
< 0.1%
47.1622 1
< 0.1%
47.1647 1
< 0.1%
47.1764 1
< 0.1%
47.1775 1
< 0.1%
47.1776 2
< 0.1%
47.1795 1
< 0.1%
47.1803 1
< 0.1%
47.1808 1
< 0.1%
ValueCountFrequency (%)
47.7776 3
< 0.1%
47.7775 3
< 0.1%
47.7774 1
 
< 0.1%
47.7772 3
< 0.1%
47.7771 2
 
< 0.1%
47.777 2
 
< 0.1%
47.7769 3
< 0.1%
47.7768 2
 
< 0.1%
47.7767 6
< 0.1%
47.7766 4
< 0.1%

long
Real number (ℝ)

High correlation 

Distinct747
Distinct (%)3.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-122.2138
Minimum-122.519
Maximum-121.315
Zeros0
Zeros (%)0.0%
Negative21210
Negative (%)100.0%
Memory size165.8 KiB
2024-11-29T16:29:36.486027image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum-122.519
5-th percentile-122.387
Q1-122.328
median-122.23
Q3-122.125
95-th percentile-121.98
Maximum-121.315
Range1.204
Interquartile range (IQR)0.203

Descriptive statistics

Standard deviation0.14055389
Coefficient of variation (CV)-0.0011500656
Kurtosis1.0355209
Mean-122.2138
Median Absolute Deviation (MAD)0.101
Skewness0.88005048
Sum-2592154.7
Variance0.019755395
MonotonicityNot monotonic
2024-11-29T16:29:36.539320image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-122.29 114
 
0.5%
-122.3 110
 
0.5%
-122.291 99
 
0.5%
-122.363 98
 
0.5%
-122.362 98
 
0.5%
-122.357 95
 
0.4%
-122.372 95
 
0.4%
-122.288 95
 
0.4%
-122.346 93
 
0.4%
-122.365 93
 
0.4%
Other values (737) 20220
95.3%
ValueCountFrequency (%)
-122.519 1
 
< 0.1%
-122.515 1
 
< 0.1%
-122.514 1
 
< 0.1%
-122.512 1
 
< 0.1%
-122.511 1
 
< 0.1%
-122.509 2
< 0.1%
-122.507 1
 
< 0.1%
-122.506 1
 
< 0.1%
-122.505 3
< 0.1%
-122.504 2
< 0.1%
ValueCountFrequency (%)
-121.315 2
< 0.1%
-121.316 1
< 0.1%
-121.319 1
< 0.1%
-121.321 1
< 0.1%
-121.325 1
< 0.1%
-121.352 2
< 0.1%
-121.359 1
< 0.1%
-121.364 2
< 0.1%
-121.402 1
< 0.1%
-121.403 1
< 0.1%

sqft_living15
Real number (ℝ)

High correlation 

Distinct757
Distinct (%)3.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1974.7783
Minimum399
Maximum5790
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size165.8 KiB
2024-11-29T16:29:36.591981image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum399
5-th percentile1140
Q11480
median1830
Q32350
95-th percentile3240
Maximum5790
Range5391
Interquartile range (IQR)870

Descriptive statistics

Standard deviation668.18396
Coefficient of variation (CV)0.33835898
Kurtosis1.3194834
Mean1974.7783
Median Absolute Deviation (MAD)410
Skewness1.0440776
Sum41885047
Variance446469.81
MonotonicityNot monotonic
2024-11-29T16:29:36.647360image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1540 193
 
0.9%
1440 190
 
0.9%
1560 190
 
0.9%
1500 179
 
0.8%
1460 167
 
0.8%
1800 165
 
0.8%
1580 165
 
0.8%
1720 165
 
0.8%
1610 164
 
0.8%
1620 163
 
0.8%
Other values (747) 19469
91.8%
ValueCountFrequency (%)
399 1
 
< 0.1%
460 2
 
< 0.1%
620 2
 
< 0.1%
670 1
 
< 0.1%
690 2
 
< 0.1%
700 2
 
< 0.1%
710 2
 
< 0.1%
720 2
 
< 0.1%
740 8
< 0.1%
750 3
 
< 0.1%
ValueCountFrequency (%)
5790 5
< 0.1%
5600 1
 
< 0.1%
5330 1
 
< 0.1%
5200 1
 
< 0.1%
5170 1
 
< 0.1%
5110 1
 
< 0.1%
5080 1
 
< 0.1%
5030 1
 
< 0.1%
5000 1
 
< 0.1%
4950 1
 
< 0.1%

sqft_lot15
Real number (ℝ)

High correlation 

Distinct8561
Distinct (%)40.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12303.153
Minimum651
Maximum560617
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size165.8 KiB
2024-11-29T16:29:36.697506image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum651
5-th percentile1971.15
Q15099.25
median7600
Q310012.75
95-th percentile36444
Maximum560617
Range559966
Interquartile range (IQR)4913.5

Descriptive statistics

Standard deviation24568.618
Coefficient of variation (CV)1.9969368
Kurtosis79.687615
Mean12303.153
Median Absolute Deviation (MAD)2492
Skewness7.8191253
Sum2.6094986 × 108
Variance6.0361697 × 108
MonotonicityNot monotonic
2024-11-29T16:29:36.747243image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5000 424
 
2.0%
4000 354
 
1.7%
6000 284
 
1.3%
7200 207
 
1.0%
4800 144
 
0.7%
7500 142
 
0.7%
8400 115
 
0.5%
4500 110
 
0.5%
3600 110
 
0.5%
5100 109
 
0.5%
Other values (8551) 19211
90.6%
ValueCountFrequency (%)
651 1
 
< 0.1%
659 1
 
< 0.1%
660 1
 
< 0.1%
748 2
< 0.1%
750 4
< 0.1%
755 1
 
< 0.1%
757 1
 
< 0.1%
758 1
 
< 0.1%
788 1
 
< 0.1%
794 1
 
< 0.1%
ValueCountFrequency (%)
560617 1
< 0.1%
438213 1
< 0.1%
434728 1
< 0.1%
392040 2
< 0.1%
380279 1
< 0.1%
360000 1
< 0.1%
358934 1
< 0.1%
339332 1
< 0.1%
335289 1
< 0.1%
326097 1
< 0.1%

year
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size165.8 KiB
2014
14313 
2015
6897 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters84840
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2014
2nd row2014
3rd row2015
4th row2014
5th row2015

Common Values

ValueCountFrequency (%)
2014 14313
67.5%
2015 6897
32.5%

Length

2024-11-29T16:29:36.792722image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-29T16:29:36.829160image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
2014 14313
67.5%
2015 6897
32.5%

Most occurring characters

ValueCountFrequency (%)
2 21210
25.0%
0 21210
25.0%
1 21210
25.0%
4 14313
16.9%
5 6897
 
8.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 84840
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 21210
25.0%
0 21210
25.0%
1 21210
25.0%
4 14313
16.9%
5 6897
 
8.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 84840
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 21210
25.0%
0 21210
25.0%
1 21210
25.0%
4 14313
16.9%
5 6897
 
8.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 84840
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 21210
25.0%
0 21210
25.0%
1 21210
25.0%
4 14313
16.9%
5 6897
 
8.1%

age
Real number (ℝ)

High correlation  Zeros 

Distinct116
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean40.932579
Minimum0
Maximum115
Zeros530
Zeros (%)2.5%
Negative0
Negative (%)0.0%
Memory size165.8 KiB
2024-11-29T16:29:36.873515image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q115
median37
Q360
95-th percentile97
Maximum115
Range115
Interquartile range (IQR)45

Descriptive statistics

Standard deviation28.79275
Coefficient of variation (CV)0.70341892
Kurtosis-0.52778385
Mean40.932579
Median Absolute Deviation (MAD)22
Skewness0.55321183
Sum868180
Variance829.02247
MonotonicityNot monotonic
2024-11-29T16:29:36.927146image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 530
 
2.5%
9 493
 
2.3%
8 470
 
2.2%
11 459
 
2.2%
10 451
 
2.1%
7 419
 
2.0%
37 384
 
1.8%
36 378
 
1.8%
47 346
 
1.6%
6 336
 
1.6%
Other values (106) 16944
79.9%
ValueCountFrequency (%)
0 530
2.5%
1 328
1.5%
2 187
 
0.9%
3 178
 
0.8%
4 130
 
0.6%
5 220
1.0%
6 336
1.6%
7 419
2.0%
8 470
2.2%
9 493
2.3%
ValueCountFrequency (%)
115 21
 
0.1%
114 53
0.2%
113 27
 
0.1%
112 31
0.1%
111 40
0.2%
110 43
0.2%
109 58
0.3%
108 70
0.3%
107 67
0.3%
106 62
0.3%

Interactions

2024-11-29T16:29:32.673276image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-29T16:29:19.513699image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-29T16:29:20.310394image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-29T16:29:20.996063image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-29T16:29:21.734545image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-29T16:29:22.450747image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-29T16:29:23.193296image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-29T16:29:24.750624image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-29T16:29:25.447680image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-29T16:29:26.137707image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-29T16:29:26.860827image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-29T16:29:27.563700image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-29T16:29:28.284591image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-29T16:29:29.026992image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-29T16:29:29.784501image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-29T16:29:30.519162image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-29T16:29:31.259842image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-29T16:29:31.969722image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-29T16:29:32.711529image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-29T16:29:19.572498image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-29T16:29:20.347876image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-29T16:29:21.036502image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-29T16:29:21.774255image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-29T16:29:22.490669image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-29T16:29:23.232017image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-29T16:29:24.789866image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-29T16:29:25.486297image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-29T16:29:26.177010image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-29T16:29:26.899399image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-29T16:29:27.602519image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-29T16:29:28.324275image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-29T16:29:29.066929image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-29T16:29:29.823365image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-29T16:29:30.562156image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-29T16:29:31.298606image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-29T16:29:32.007534image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-29T16:29:32.749311image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-29T16:29:19.639997image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-29T16:29:20.383010image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-29T16:29:21.073498image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-29T16:29:21.810785image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-29T16:29:22.529012image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-29T16:29:23.270034image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-29T16:29:24.825586image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-29T16:29:25.520532image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-29T16:29:26.215296image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-29T16:29:26.934591image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-29T16:29:27.639882image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-29T16:29:28.362160image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-29T16:29:29.105674image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-29T16:29:29.861429image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-29T16:29:30.600369image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-29T16:29:31.334618image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-29T16:29:32.043606image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-29T16:29:32.791806image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-29T16:29:19.691047image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-29T16:29:20.422728image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-29T16:29:21.116432image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-29T16:29:21.852063image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-29T16:29:22.572390image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-29T16:29:23.313890image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-29T16:29:24.866605image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-29T16:29:25.560692image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-29T16:29:26.256767image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-29T16:29:26.976368image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-29T16:29:27.681562image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-29T16:29:28.406130image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-29T16:29:29.149535image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-29T16:29:29.902658image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-29T16:29:30.646278image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-29T16:29:31.376290image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-29T16:29:32.085303image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-29T16:29:32.830455image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-29T16:29:19.738986image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-29T16:29:20.460215image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-29T16:29:21.155889image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-29T16:29:21.890497image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-29T16:29:22.614677image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-29T16:29:23.355075image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-29T16:29:24.905350image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-29T16:29:25.598548image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-29T16:29:26.295984image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-29T16:29:27.016266image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-29T16:29:27.721578image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-29T16:29:28.447007image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-29T16:29:29.192081image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-29T16:29:29.941946image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-29T16:29:30.690762image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-29T16:29:31.414764image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-29T16:29:32.124125image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-29T16:29:32.871234image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-29T16:29:19.794571image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-29T16:29:20.500882image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-29T16:29:21.200153image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-29T16:29:21.932539image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-29T16:29:22.655473image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-29T16:29:23.397364image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-29T16:29:24.945737image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-29T16:29:25.638847image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-29T16:29:26.337241image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-29T16:29:27.056502image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-29T16:29:27.763568image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-29T16:29:28.488578image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-29T16:29:29.234528image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-29T16:29:29.982045image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-29T16:29:30.735818image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-29T16:29:31.454246image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-29T16:29:32.163402image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-29T16:29:32.912561image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-29T16:29:19.833246image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-29T16:29:20.538850image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-29T16:29:21.240970image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-29T16:29:21.972624image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-29T16:29:22.696591image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-29T16:29:23.438971image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-29T16:29:24.984455image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-29T16:29:25.677385image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-29T16:29:26.378241image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-29T16:29:27.096130image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-29T16:29:27.803414image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-29T16:29:28.531489image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-29T16:29:29.280912image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-29T16:29:30.021795image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-29T16:29:30.776743image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-29T16:29:31.496095image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-29T16:29:32.203024image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-29T16:29:32.950318image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-29T16:29:19.874000image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-29T16:29:20.575061image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-29T16:29:21.281072image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-29T16:29:22.010691image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-29T16:29:22.737746image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-29T16:29:23.477530image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-29T16:29:25.020473image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-29T16:29:25.714026image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-29T16:29:26.417494image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-29T16:29:27.132898image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-29T16:29:27.841392image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-29T16:29:28.570794image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-29T16:29:29.320657image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-29T16:29:30.060733image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-29T16:29:30.818086image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-29T16:29:31.534793image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-29T16:29:32.240289image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-29T16:29:32.986865image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-29T16:29:19.911444image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-29T16:29:20.610428image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-29T16:29:21.320400image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-29T16:29:22.048284image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-29T16:29:22.778210image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-29T16:29:23.516457image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-29T16:29:25.056977image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-29T16:29:25.749893image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-29T16:29:26.457114image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-29T16:29:27.170286image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-29T16:29:27.879075image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-29T16:29:28.611266image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-29T16:29:29.360615image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-29T16:29:30.099303image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-29T16:29:30.858042image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-29T16:29:31.572084image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-29T16:29:32.277749image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-29T16:29:33.028343image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-29T16:29:19.951139image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-29T16:29:20.649876image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-29T16:29:21.362313image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-29T16:29:22.090349image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-29T16:29:22.819664image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-29T16:29:23.557801image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-29T16:29:25.096849image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-29T16:29:25.790348image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-29T16:29:26.498005image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-29T16:29:27.212258image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-29T16:29:27.920865image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-29T16:29:28.653508image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-29T16:29:29.404179image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-29T16:29:30.140285image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-29T16:29:30.903745image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-29T16:29:31.612435image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-29T16:29:32.317904image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-29T16:29:33.065702image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-29T16:29:19.989837image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-29T16:29:20.688351image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-29T16:29:21.402200image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-29T16:29:22.128215image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-29T16:29:22.860072image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-29T16:29:23.597064image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-29T16:29:25.134014image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-29T16:29:25.826986image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-29T16:29:26.536112image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-29T16:29:27.249492image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-29T16:29:27.958105image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-29T16:29:28.692657image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-29T16:29:29.446467image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-29T16:29:30.177958image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-29T16:29:30.941516image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-29T16:29:31.650279image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-29T16:29:32.355703image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-29T16:29:33.105538image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-29T16:29:20.030263image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-29T16:29:20.725253image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-29T16:29:21.443939image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-29T16:29:22.167244image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-29T16:29:22.900205image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-29T16:29:23.636710image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-29T16:29:25.173572image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-29T16:29:25.865664image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-29T16:29:26.576886image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-29T16:29:27.287845image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-29T16:29:27.998425image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-29T16:29:28.735110image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-29T16:29:29.488298image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-29T16:29:30.217946image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-29T16:29:30.981618image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-29T16:29:31.689745image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-29T16:29:32.395446image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-29T16:29:33.149566image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-29T16:29:20.072753image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-29T16:29:20.767452image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-29T16:29:21.489005image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-29T16:29:22.210830image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-29T16:29:22.944959image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-29T16:29:24.507326image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-29T16:29:25.215648image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-29T16:29:25.907468image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-29T16:29:26.619570image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-29T16:29:27.328897image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-29T16:29:28.040782image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-29T16:29:28.779027image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-29T16:29:29.532771image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-29T16:29:30.259864image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-29T16:29:31.023139image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-29T16:29:31.733203image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-29T16:29:32.436391image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-29T16:29:33.192717image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-29T16:29:20.116605image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-29T16:29:20.810238image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-29T16:29:21.532556image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-29T16:29:22.253992image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-29T16:29:22.990131image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-29T16:29:24.552895image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-29T16:29:25.257380image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-29T16:29:25.948805image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-29T16:29:26.663009image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-29T16:29:27.372093image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-29T16:29:28.084393image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-29T16:29:28.825229image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-29T16:29:29.577206image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-29T16:29:30.302643image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-29T16:29:31.066074image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-29T16:29:31.774898image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-29T16:29:32.478937image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-29T16:29:33.231321image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-29T16:29:20.156928image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-29T16:29:20.848402image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-29T16:29:21.572162image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-29T16:29:22.293443image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-29T16:29:23.030893image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-29T16:29:24.592845image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-29T16:29:25.296266image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-29T16:29:25.986907image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-29T16:29:26.702737image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-29T16:29:27.411196image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-29T16:29:28.125034image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-29T16:29:28.866196image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-29T16:29:29.619419image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-29T16:29:30.342647image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-29T16:29:31.107420image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-29T16:29:31.814564image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-29T16:29:32.518654image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-29T16:29:33.271339image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-29T16:29:20.193905image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-29T16:29:20.884447image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-29T16:29:21.612775image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-29T16:29:22.332724image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-29T16:29:23.072029image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-29T16:29:24.632815image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-29T16:29:25.333517image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-29T16:29:26.024563image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-29T16:29:26.743444image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-29T16:29:27.448778image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-29T16:29:28.165510image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-29T16:29:28.906216image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-29T16:29:29.660681image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-29T16:29:30.386601image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-29T16:29:31.144345image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-29T16:29:31.852320image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-29T16:29:32.557851image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-29T16:29:33.308542image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-29T16:29:20.232306image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-29T16:29:20.922895image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-29T16:29:21.651801image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-29T16:29:22.371615image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-29T16:29:23.111729image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-29T16:29:24.671973image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-29T16:29:25.370631image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-29T16:29:26.063042image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-29T16:29:26.782043image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-29T16:29:27.486742image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-29T16:29:28.205665image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-29T16:29:28.945990image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-29T16:29:29.704013image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-29T16:29:30.432928image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-29T16:29:31.183097image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-29T16:29:31.892185image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-29T16:29:32.596539image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-29T16:29:33.346567image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-29T16:29:20.270457image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-29T16:29:20.958918image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-29T16:29:21.693683image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-29T16:29:22.409293image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-29T16:29:23.154165image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-29T16:29:24.710741image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-29T16:29:25.407831image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-29T16:29:26.100354image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-29T16:29:26.821482image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-29T16:29:27.524826image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-29T16:29:28.244776image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-29T16:29:28.985032image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-29T16:29:29.743111image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-29T16:29:30.475871image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-29T16:29:31.221326image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-29T16:29:31.930443image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-11-29T16:29:32.632852image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Correlations

2024-11-29T16:29:36.973409image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
idpricebedroomsbathroomssqft_livingsqft_lotfloorswaterfrontviewconditiongradesqft_abovesqft_basementyr_builtyr_renovatedzipcodelatlongsqft_living15sqft_lot15yearage
id1.000-0.0170.0030.009-0.009-0.1360.0190.0020.019-0.0240.011-0.008-0.0030.022-0.016-0.009-0.0030.021-0.001-0.1430.011-0.018
price-0.0171.0000.3150.5030.6750.0690.2630.1080.3240.0380.6700.5830.2740.0580.100-0.0520.3440.0230.5890.0600.004-0.101
bedrooms0.0030.3151.0000.5210.5990.0250.173-0.0290.0660.0260.3600.4880.2970.1660.012-0.162-0.0180.1370.4000.022-0.009-0.174
bathrooms0.0090.5030.5211.0000.7400.0660.5030.0040.143-0.1320.6510.6670.2490.5220.037-0.2080.0180.2270.5560.060-0.028-0.549
sqft_living-0.0090.6750.5990.7401.0000.1520.3540.0120.226-0.0640.7520.8690.3960.3360.034-0.2090.0460.2500.7560.155-0.031-0.355
sqft_lot-0.1360.0690.0250.0660.1521.000-0.0160.0190.046-0.0090.0960.166-0.0040.0550.001-0.134-0.0870.2300.1380.695-0.004-0.052
floors0.0190.2630.1730.5030.354-0.0161.0000.0030.011-0.2710.4570.527-0.2670.4960.001-0.0600.0470.1260.276-0.023-0.024-0.510
waterfront0.0020.108-0.0290.0040.0120.0190.0031.0000.2920.0170.0180.0030.018-0.0210.0160.037-0.019-0.0370.0280.0200.0020.019
view0.0190.3240.0660.1430.2260.0460.0110.2921.0000.0460.2060.1130.244-0.0560.0600.0910.004-0.0820.2450.0450.0010.036
condition-0.0240.0380.026-0.132-0.064-0.009-0.2710.0170.0461.000-0.154-0.1680.184-0.365-0.0610.003-0.016-0.106-0.098-0.000-0.0460.398
grade0.0110.6700.3600.6510.7520.0960.4570.0180.206-0.1541.0000.7460.1260.458-0.003-0.1890.1110.2000.7030.099-0.034-0.467
sqft_above-0.0080.5830.4880.6670.8690.1660.5270.0030.113-0.1680.7461.000-0.1100.4410.007-0.272-0.0090.3560.7300.171-0.027-0.448
sqft_basement-0.0030.2740.2970.2490.396-0.004-0.2670.0180.2440.1840.126-0.1101.000-0.1440.0550.0840.108-0.1570.164-0.007-0.0120.119
yr_built0.0220.0580.1660.5220.3360.0550.496-0.021-0.056-0.3650.4580.441-0.1441.000-0.220-0.346-0.1510.4080.3330.0720.002-0.913
yr_renovated-0.0160.1000.0120.0370.0340.0010.0010.0160.060-0.061-0.0030.0070.055-0.2201.0000.0640.030-0.065-0.022-0.000-0.026-0.161
zipcode-0.009-0.052-0.162-0.208-0.209-0.134-0.0600.0370.0910.003-0.189-0.2720.084-0.3460.0641.0000.268-0.564-0.283-0.1530.0040.320
lat-0.0030.344-0.0180.0180.046-0.0870.047-0.0190.004-0.0160.111-0.0090.108-0.1510.0300.2681.000-0.1370.045-0.088-0.0300.138
long0.0210.0230.1370.2270.2500.2300.126-0.037-0.082-0.1060.2000.356-0.1570.408-0.065-0.564-0.1371.0000.3400.257-0.002-0.383
sqft_living15-0.0010.5890.4000.5560.7560.1380.2760.0280.245-0.0980.7030.7300.1640.333-0.022-0.2830.0450.3401.0000.175-0.024-0.325
sqft_lot15-0.1430.0600.0220.0600.1550.695-0.0230.0200.045-0.0000.0990.171-0.0070.072-0.000-0.153-0.0880.2570.1751.000-0.008-0.068
year0.0110.004-0.009-0.028-0.031-0.004-0.0240.0020.001-0.046-0.034-0.027-0.0120.002-0.0260.004-0.030-0.002-0.024-0.0081.0000.025
age-0.018-0.101-0.174-0.549-0.355-0.052-0.5100.0190.0360.398-0.467-0.4480.119-0.913-0.1610.3200.138-0.383-0.325-0.0680.0251.000
2024-11-29T16:29:37.067710image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
idpricebedroomsbathroomssqft_livingsqft_lotfloorswaterfrontviewconditiongradesqft_abovesqft_basementyr_builtyr_renovatedzipcodelatlongsqft_living15sqft_lot15yearage
id1.0000.0810.0410.0730.0790.1210.0810.0170.0690.0710.1120.0910.0840.1620.0340.3500.2680.3260.1160.1050.0110.153
price0.0811.0000.2490.5840.6970.0000.2430.1610.3870.1010.6680.6570.3200.1910.1290.3120.5190.2380.5730.0000.0000.211
bedrooms0.0410.2491.0000.5580.5210.0000.2790.0280.0650.0910.4160.4520.2820.2770.0250.2210.1370.2020.3450.0000.0090.281
bathrooms0.0730.5840.5581.0000.7620.0030.4470.0000.2080.2700.6950.6890.3660.5750.0920.3180.2500.3040.5700.0260.0370.584
sqft_living0.0790.6970.5210.7621.0000.0970.3790.0380.2750.1720.7610.9570.5870.4220.0540.3390.2310.3230.7660.0780.0330.438
sqft_lot0.1210.0000.0000.0030.0971.0000.0300.0440.0630.0720.0830.1260.0550.0870.0000.1280.1800.2310.0760.5700.0000.092
floors0.0810.2430.2790.4470.3790.0301.0000.0250.0230.2620.3680.5050.2470.6220.0750.3400.2500.3060.3350.0530.0320.612
waterfront0.0170.1610.0280.0000.0380.0440.0251.0000.3800.0200.0300.0180.0220.0360.0190.1020.0540.1170.0540.0000.0000.034
view0.0690.3870.0650.2080.2750.0630.0230.3801.0000.0650.2400.1480.3100.1060.0530.1700.1560.1940.2980.0550.0000.079
condition0.0710.1010.0910.2700.1720.0720.2620.0200.0651.0000.3490.2590.2310.5320.0560.1770.1380.1910.1510.0140.0420.554
grade0.1120.6680.4160.6950.7610.0830.3680.0300.2400.3491.0000.7570.2530.4980.0290.3270.3270.2980.7310.0540.0370.504
sqft_above0.0910.6570.4520.6890.9570.1260.5050.0180.1480.2590.7571.0000.2830.5360.0520.4270.2300.4020.7480.0940.0300.541
sqft_basement0.0840.3200.2820.3660.5870.0550.2470.0220.3100.2310.2530.2831.0000.3130.0780.2340.1710.2300.3070.0000.0200.283
yr_built0.1620.1910.2770.5750.4220.0870.6220.0360.1060.5320.4980.5360.3131.0000.2970.6220.4460.5420.4070.0720.0200.997
yr_renovated0.0340.1290.0250.0920.0540.0000.0750.0190.0530.0560.0290.0520.0780.2971.0000.1150.0770.0980.0170.0000.0380.221
zipcode0.3500.3120.2210.3180.3390.1280.3400.1020.1700.1770.3270.4270.2340.6220.1151.0000.7940.7860.4510.1310.0160.606
lat0.2680.5190.1370.2500.2310.1800.2500.0540.1560.1380.3270.2300.1710.4460.0770.7941.0000.4920.2860.1350.0440.438
long0.3260.2380.2020.3040.3230.2310.3060.1170.1940.1910.2980.4020.2300.5420.0980.7860.4921.0000.4330.2120.0250.527
sqft_living150.1160.5730.3450.5700.7660.0760.3350.0540.2980.1510.7310.7480.3070.4070.0170.4510.2860.4331.0000.0870.0220.402
sqft_lot150.1050.0000.0000.0260.0780.5700.0530.0000.0550.0140.0540.0940.0000.0720.0000.1310.1350.2120.0871.0000.0000.073
year0.0110.0000.0090.0370.0330.0000.0320.0000.0000.0420.0370.0300.0200.0200.0380.0160.0440.0250.0220.0001.0000.051
age0.1530.2110.2810.5840.4380.0920.6120.0340.0790.5540.5040.5410.2830.9970.2210.6060.4380.5270.4020.0730.0511.000
2024-11-29T16:29:37.153980image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
agebathroomsbedroomsconditionfloorsgradeidlatlongpricesqft_abovesqft_basementsqft_livingsqft_living15sqft_lotsqft_lot15viewwaterfrontyearyr_builtyr_renovatedzipcode
age1.000-0.595-0.1870.262-0.579-0.512-0.0230.113-0.371-0.144-0.4850.159-0.372-0.3260.0730.0520.0330.0260.039-0.916-0.1730.277
bathrooms-0.5951.0000.5170.1150.5450.6510.0170.0040.2630.4840.6840.1790.7400.5630.0550.0510.0880.0000.0280.5740.032-0.205
bedrooms-0.1870.5171.0000.0520.2220.3750.007-0.0270.1920.3370.5360.2220.6460.4410.2130.1990.0380.0280.0090.1810.012-0.168
condition0.2620.1150.0521.0000.1810.1560.0300.0580.0810.0420.1100.0980.0720.0630.0300.0080.0240.0250.0510.2490.0690.074
floors-0.5790.5450.2220.1811.0000.4990.0190.0220.1500.3150.597-0.2840.3960.301-0.245-0.2410.0150.0180.0230.5590.007-0.062
grade-0.5120.6510.3750.1560.4991.0000.0210.1000.2230.6490.7060.0770.7100.6560.1390.1440.1100.0260.0280.5040.003-0.181
id-0.0230.0170.0070.0300.0190.0211.000-0.0050.0080.0050.0050.0020.0030.001-0.115-0.1140.0290.0130.0080.027-0.016-0.006
lat0.1130.004-0.0270.0580.0220.100-0.0051.000-0.1440.461-0.0310.1140.0260.024-0.123-0.1170.0650.0410.033-0.1280.0260.250
long-0.3710.2630.1920.0810.1500.2230.008-0.1441.0000.0610.389-0.2060.2870.3840.3740.3750.0820.0900.0190.410-0.071-0.576
price-0.1440.4840.3370.0420.3150.6490.0050.4610.0611.0000.5300.2400.6350.5620.0590.0480.1710.1230.0000.0980.088-0.004
sqft_above-0.4850.6840.5360.1100.5970.7060.005-0.0310.3890.5301.000-0.1860.8400.6930.2630.2460.0620.0140.0230.4770.019-0.281
sqft_basement0.1590.1790.2220.098-0.2840.0770.0020.114-0.2060.240-0.1861.0000.3160.1150.0260.0200.1340.0170.015-0.1830.0520.119
sqft_living-0.3720.7400.6460.0720.3960.7100.0030.0260.2870.6350.8400.3161.0000.7430.2940.2750.1170.0290.0260.3560.038-0.208
sqft_living15-0.3260.5630.4410.0630.3010.6560.0010.0240.3840.5620.6930.1150.7431.0000.3510.3580.1280.0420.0170.338-0.021-0.289
sqft_lot0.0730.0550.2130.030-0.2450.139-0.115-0.1230.3740.0590.2630.0260.2940.3511.0000.9220.0260.0340.000-0.041-0.005-0.322
sqft_lot150.0520.0510.1990.008-0.2410.144-0.114-0.1170.3750.0480.2460.0200.2750.3580.9221.0000.0310.0000.000-0.020-0.004-0.328
view0.0330.0880.0380.0240.0150.1100.0290.0650.0820.1710.0620.1340.1170.1280.0260.0311.0000.4630.0000.0440.0650.071
waterfront0.0260.0000.0280.0250.0180.0260.0130.0410.0900.1230.0140.0170.0290.0420.0340.0000.4631.0000.0000.0290.0120.078
year0.0390.0280.0090.0510.0230.0280.0080.0330.0190.0000.0230.0150.0260.0170.0000.0000.0000.0001.0000.0150.0240.012
yr_built-0.9160.5740.1810.2490.5590.5040.027-0.1280.4100.0980.477-0.1830.3560.338-0.041-0.0200.0440.0290.0151.000-0.210-0.315
yr_renovated-0.1730.0320.0120.0690.0070.003-0.0160.026-0.0710.0880.0190.0520.038-0.021-0.005-0.0040.0650.0120.024-0.2101.0000.064
zipcode0.277-0.205-0.1680.074-0.062-0.181-0.0060.250-0.576-0.004-0.2810.119-0.208-0.289-0.322-0.3280.0710.0780.012-0.3150.0641.000

Missing values

2024-11-29T16:29:33.411419image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
A simple visualization of nullity by column.
2024-11-29T16:29:33.535648image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

iddatepricebedroomsbathroomssqft_livingsqft_lotfloorswaterfrontviewconditiongradesqft_abovesqft_basementyr_builtyr_renovatedzipcodelatlongsqft_living15sqft_lot15yearage
071293005202014-10-13221900.031.00118056501.0003711800195509817847.5112-122.25713405650201459
164141001922014-12-09538000.032.25257072422.000372170400195119919812547.7210-122.31916907639201423
256315004002015-02-25180000.021.00770100001.000367700193309802847.7379-122.23327208062201582
324872008752014-12-09604000.043.00196050001.000571050910196509813647.5208-122.39313605000201449
419544005102015-02-18510000.032.00168080801.0003816800198709807447.6168-122.04518007503201528
513214000602014-06-27257500.032.25171568192.0003717150199509800347.3097-122.32722386819201419
620080002702015-01-15291850.031.50106097111.0003710600196309819847.4095-122.31516509711201552
724146001262015-04-15229500.031.00178074701.000371050730196009814647.5123-122.33717808113201555
837935001602015-03-12323000.032.50189065602.0003718900200309803847.3684-122.03123907570201512
917368005202015-04-03662500.032.50356097961.0003818601700196509800747.6007-122.14522108925201550
iddatepricebedroomsbathroomssqft_livingsqft_lotfloorswaterfrontviewconditiongradesqft_abovesqft_basementyr_builtyr_renovatedzipcodelatlongsqft_living15sqft_lot15yearage
2120078521400402014-08-25507250.032.50227055362.0003822700200309806547.5389-121.88122705731201411
2120198342013672015-01-26429000.032.00149011263.0003814900201409814447.5699-122.2881400123020151
2120234489002102014-10-14610685.042.50252060232.0003925200201409805647.5137-122.1672520602320140
2120379360004292015-03-261007500.043.50351072002.000392600910200909813647.5537-122.3982050620020156
2120429978000212015-02-19475000.032.50131012942.000381180130200809811647.5773-122.4091330126520157
212052630000182014-05-21360000.032.50153011313.0003815300200909810347.6993-122.3461530150920145
2120666000601202015-02-23400000.042.50231058132.0003823100201409814647.5107-122.3621830720020151
2120715233001412014-06-23402101.020.75102013502.0003710200200909814447.5944-122.2991020200720145
212082913101002015-01-16400000.032.50160023882.0003816000200409802747.5345-122.06914101287201511
2120915233001572014-10-15325000.020.75102010762.0003710200200809814447.5941-122.2991020135720146